Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices

Healthcare Technology Letters

article

Ramalingaswamy Cheruku¹  Damodar Reddy Edla¹  Venkatanareshbabu Kuppili¹  Ramesh Dharavath²  Nareshkumar Reddy Beechu³ 

[1] Department of Computer Science and Engineering, National Institute of Technology Goa;Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines);Department of Electronics and Communication Engineering, National Institute of Technology Goa

关键词: diseases;    patient diagnosis;    neural nets;    random-access storage;    genetic algorithms;    biology computing;    automatic disease diagnosis;    optimised weightless neural networks;    low power wearable devices;    noninvasive devices;    pain free devices;    quality of life;    memory constraint;    variable sized random access memories;    modified binary TRIE data structure;    bioinspired based genetic algorithm;   

DOI  :  10.1049/htl.2017.0003

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.

【 授权许可】

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